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@masak
masak / explanation.md
Last active September 20, 2026 03:51
How is git commit sha1 formed

(The below text is licensed with CC0, which means that if you want to use or translate it, that is OK by me.)

Ok, I geeked out, and this is probably more information than you need. But it completely answers the question. Sorry. ☺

Locally, I'm at this commit:

$ git show
commit d6cd1e2bd19e03a81132a23b2025920577f84e37
Author: jnthn <jnthn@jnthn.net>

Date: Sun Apr 15 16:35:03 2012 +0200

@aleclarson
aleclarson / solid-2-versus-octane.md
Last active September 20, 2026 03:51
Solid 2 vs Octane

NOTICE: I've summarized the perspectives of Octane's and Solid's creators here, for anyone short on time: https://gist.github.com/aleclarson/829c10aa7d287944ef75e1c2a90bef06


I'm a React/Preact/Astro developer with an interest in other renderers. The two that have most caught my eye recently are Octane (from Dominic Gannaway of React Hooks / Svelte / Inferno fame) and Solid 2.

I asked ChatGPT to compare them with its own "Renderer DX Pillars", which I've shared below. This is its gut instinct, with very little hedging:

Pillar Winner Why
@vitiko98
vitiko98 / get_keys.py
Last active September 20, 2026 03:38
Get Qobuz App ID and Secrets
from qobuz_dl.bundle import Bundle
bundle = Bundle()
app_id = bundle.get_app_id()
secrets = "\n".join(bundle.get_secrets().values())
print(f"App ID: {app_id}")
print("#" * 20)
@robert-hoffmann
robert-hoffmann / doc-add.prompt.md
Last active September 20, 2026 03:38
Copilot prompt and instructions for clean code
mode ask
description Document a Python file in the project, following specific conventions for style, formatting, and documentation.

Please document the file ${file} following the conventions observed in the project.

CRITICAL: Code Preservation Policy

  • NEVER remove any existing functionality from the code
  • NEVER remove or delete any commented-out sections
@AxonOS-BCI
AxonOS-BCI / deterministic-runtime-audit.md
Created September 20, 2026 03:28
What a deterministic-runtime audit should establish - DY Research, revised September 2026

I Audited a Rust Real-Time System for Determinism. Here's What Actually Matters.

Why "microsecond latency" is not a real-time guarantee — and what a serious deterministic-runtime audit should establish

Revised — September 2026


A system can report 2 μs average latency and still miss a 100 μs deadline.

name explain-diff-html
description Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

@NetSysFire
NetSysFire / rendering-isometric-tilesets.md
Last active September 20, 2026 03:29
Creating isometric tilesets with Blender

Creating isometric tilesets with Blender

floor

Unfortunately, I have found no such guide when I attempted this, so I am writing this down for posterity and so no one has to reinvent the wheel.

This method is ideal for me since I have 3D experience but absolutely suck at a 2D workflow, yet I need 2D assets for a 2D game.

Tested with Blender 4.4.0.

@karpathy
karpathy / microgpt.py
Last active September 20, 2026 03:36
microgpt
"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@pjburnhill
pjburnhill / TypeSafe Jev — Project Knowledge Resource.md
Created September 16, 2026 23:14
Comprehensive project reference for TypeSafe Jev: concepts, architecture, primitives, strengths, limitations, use cases, patterns, and practical guidance.

TypeSafe Jev — Project Knowledge Resource

Status: Working project reference
Knowledge date: 16 September 2026
Scope: TypeSafe AI, System One Models, and specifically the Jev model


1. Purpose of this resource

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.